The ability to recognize many different ways of expressing the same or similar meaning is important to many Natural Language Processing (NLP) applications, such as question answering, searching, etc. Paraphrase acquisition is the process used to address this issue. However, the current technology and approaches implementing paraphrase acquisition are inefficient and/or inadequate.
In this regard, paraphrase corpora are typically obtained either through manual annotations or through machine learning. Manual annotation of paraphrases is typically expensive and time consuming, while machine learned paraphrases are often error prone.